A real-time anomaly detection algorithm/or water quality data using dual time-moving windows
Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a novel anomaly detection algorithm for water quality data using dual time-moving windows, which can identify anomaly data from historical pattern...
Uloženo v:
| Vydáno v: | 2017 Seventh International Conference on Innovative Computing Technology (INTECH) s. 36 - 41 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.08.2017
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a novel anomaly detection algorithm for water quality data using dual time-moving windows, which can identify anomaly data from historical patterns in real-time. The algorithm is based on an autoregressive linear combination model, a prediction interval with dual time-moving windows and a backtracking verification strategy. We have tested the algorithm using 3-month water quality data of PH from a real water quality monitoring station in a river system. Experimental results show that our novel anomaly detection algorithm can significantly decrease the rate of false positive and has better anomaly detection performance than AD and ADAM algorithms. |
|---|---|
| AbstractList | Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a novel anomaly detection algorithm for water quality data using dual time-moving windows, which can identify anomaly data from historical patterns in real-time. The algorithm is based on an autoregressive linear combination model, a prediction interval with dual time-moving windows and a backtracking verification strategy. We have tested the algorithm using 3-month water quality data of PH from a real water quality monitoring station in a river system. Experimental results show that our novel anomaly detection algorithm can significantly decrease the rate of false positive and has better anomaly detection performance than AD and ADAM algorithms. |
| Author | Jin Zhang Wong, Prudence W. H. Yong Yue Xiaohui Zhu |
| Author_xml | – sequence: 1 surname: Jin Zhang fullname: Jin Zhang email: sgjzha31@liverpool.ac.uk organization: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China – sequence: 2 surname: Xiaohui Zhu fullname: Xiaohui Zhu email: bobzhu@liverpool.ac.uk organization: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China – sequence: 3 surname: Yong Yue fullname: Yong Yue email: yong.yue@xjtlu.edu.cn organization: Dept. of Comput. Sci. & Software Eng., Xi'an Jiaotong-Liverpool Univ., Suzhou, China – sequence: 4 givenname: Prudence W. H. surname: Wong fullname: Wong, Prudence W. H. email: pwong@liverpool.ac.uk organization: Dept. of Comput. Sci., Univ. of Liverpool, Liverpool, UK |
| BookMark | eNotj8tqAjEYRlNoF9X2CdzkBWbMZS7JUgZbBbGbWRbkn-SPDcwk7Rg7-PZV6uqDw8eBMyOPIQYkZMFZzjnTy-2-XTebXDBe54ozUQj-QGa8ZJpJrbR-Jp8rOiL0WfIDUghxgP5CLSY0ycdAoT_G0aevYRlHOkHCkf6coffpeoIE9Hzy4UjtFdGbIRvi7w1MPtg4nV7Ik4P-hK_3nZP2bd02m2z38b5tVrvMa5YyI6RSxpWiq6C2puZgEAslURadVdxxWxZcaSeY44UBYStVauM6QGMqhkrOyeJf6xHx8D36AcbL4d4r_wC5Y1H9 |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/INTECH.2017.8102421 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Xplore IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 1509039899 9781509039890 |
| EndPage | 41 |
| ExternalDocumentID | 8102421 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i90t-c2388cf52b6a7dc71acee483e34bd81f1d54189f20f14ca2d6859cfbaecc60e83 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jun 29 18:37:25 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i90t-c2388cf52b6a7dc71acee483e34bd81f1d54189f20f14ca2d6859cfbaecc60e83 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_8102421 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-Aug. |
| PublicationDateYYYYMMDD | 2017-08-01 |
| PublicationDate_xml | – month: 08 year: 2017 text: 2017-Aug. |
| PublicationDecade | 2010 |
| PublicationTitle | 2017 Seventh International Conference on Innovative Computing Technology (INTECH) |
| PublicationTitleAbbrev | INTECH |
| PublicationYear | 2017 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.6446856 |
| Snippet | Anomaly data in real-time water quality monitoring systems can cause false alarms and significantly decrease system stability and reliability. We propose a... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 36 |
| SubjectTerms | anomaly data detection autoregressive linear combination model backtracking verification Computers Conferences dual time-moving windows |
| Title | A real-time anomaly detection algorithm/or water quality data using dual time-moving windows |
| URI | https://ieeexplore.ieee.org/document/8102421 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV29SwMxFA-1ODiptOI3GRxNe7lLLskoxdKpdOjQQSj5rIX2Tq5Xi_-9Se6oCC5uIYQkvDe8r9_vPQCeFKWGuYyilJgUEasZUjr3wUpKHQ-lJyJ0HDbBplO-WIhZBzwfuTDW2gg-s4OwjLV8U-p9SJUNOQ4Wxcc6J4zlDVerbSSEEzEMPWRHk4DWYoP25K-RKdFijM__99YF6P9Q7-DsaFQuQccWPfD2Ar1vt0FhEDyURbmVmy9obB1hVAWUm1XpY_z37bCs4ME7jxVsuJL-kKwlDNj2FQykKxhuQNuYRYAHH46Xh10fzMev89EEtWMR0FokNdLeyHLtaKpyyYxmWPovEZ7ZjCjDscOGEsyFSxOHiZapyTkV2inplZUnlmdXoFuUhb0GUDLiuEoUFsQQyx03WllnVW6MI5nmN6AXBLP8aBpfLFuZ3P69fQfOguwbdNw96NbV3j6AU_1Zr3fVY9TWN1Yvm0Q |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA9jCnpS2cRvc_BotqZNmvQowzFxjh122EEY-ZyDrR1d5_C_N2nLRPDiLYRHEvIO7-v3ew-AB0mpZjaiKCQ6RMQohqSKXbASUst96Ykkqhw2wUYjPp0m4wZ43HNhjDEl-Mx0_LKs5etMbX2qrMuxtygu1jmghIRBxdaqWwnhIOn6LrK9gcdrsU4t-2toSmkz-if_u-0UtH_Id3C8NytnoGHSFnh_gs67WyI_Ch6KNFuJ5RfUpiiBVCkUy3nmovyPVTfL4c65jzms2JJOSBQCenT7HHraFfQnoFWZR4A7F5Bnu00bTPrPk94A1YMR0CIJCqScmeXK0lDGgmnFsHBPIjwyEZGaY4s1JZgnNgwsJkqEOuY0UVYKp644MDw6B800S80FgIIRy2UgcUI0MdxyraSxRsZaWxIpfgla_mNm66r1xaz-k6u_t-_B0WDyNpwNX0av1-DY66HCyt2AZpFvzS04VJ_FYpPflZr7BpMvnos |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2017+Seventh+International+Conference+on+Innovative+Computing+Technology+%28INTECH%29&rft.atitle=A+real-time+anomaly+detection+algorithm%2For+water+quality+data+using+dual+time-moving+windows&rft.au=Jin+Zhang&rft.au=Xiaohui+Zhu&rft.au=Yong+Yue&rft.au=Wong%2C+Prudence+W.+H.&rft.date=2017-08-01&rft.pub=IEEE&rft.spage=36&rft.epage=41&rft_id=info:doi/10.1109%2FINTECH.2017.8102421&rft.externalDocID=8102421 |